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ORC

New ORC group forms in Tulsa

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04/09/2012

TULSA, Okla.—A new group in this city is the most recent addition to the growing list of public-private partnerships forming around the country to combat organized retail crime.

New ORC group forms in Tulsa

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Thursday, April 5, 2012

The rise in public-private partnerships to fight organized retail crime continues. The Tulsa Police Department has created a new Organized Retail Crime Unit, which is collaborating with local retailers to combat professional shoplifters, according to a news report from KTUL. Detective Lori Visser, a member of the newly formed ORC unit, said the group has already identified between 40 and 60 organized shoplifting groups operating in and around the city. She also said ORC has cost the local taxpayers more than $1 million in lost tax revenue.

With the increasing number of these ORC groups, I think it's time we had a clearinghouse of information about all these groups and points of contact at each.

Wisconsin bill places ORC in the crosshairs

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03/14/2012

MADISON, Wis.—Add Wisconsin to the list of states trying to raise the stakes in the battle against organized retail crime.

Cabela's forms investigations team to combat ORC

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03/06/2012

SIDNEY, Neb.—National outdoor retailer Cabela’s has formed a special investigations team that will tackle organized retail crime affecting its 38 locations in North America.

Business intelligence: PacSun's journey into predictive analytics

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06/13/2011

DALLAS—The opening session on the second day of the National Retail Federation Loss Prevention Conference here didn’t bring good news for retailers. Shrink is on the rise, said Professor Richard Hollinger from the University of Florida, who released preliminary findings from the annual National Retail Security Survey

Nearly 95 percent of retailers report ORC, up 6 percent from last year

 - 
06/13/2011

WASHINGTON—Incidents of organized retail crime is on the rise for retailers around the country and thieves are becoming more violent and brazen, according to the results of the seventh annual Organized Retail Crime survey.

Security at luxury mall 'pays attention to detail'

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05/09/2011

ORLANDO—Walking into The Mall at Millenia, a luxury mall with 150 stores including high-end merchants such as one of only two Rolex storefronts in the country, uniformed security officers greet guests. That is intentional, said Gregg Moore, security director of The Mall at Millenia.

Ahead of the game: Grocery chain gets involved it opens the doors

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05/02/2011

NORTHBOROUGH, Mass.—Wegmans Food Markets won’t even open its doors here until October, but already the grocery chain has made a concerted effort to be an active member of the regional retail community.

How the science of statistics creates profitable solutions in retail loss prevention

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Monday, April 18, 2011

By David Speights, Ph.D., and Chris Hanks, Ph.D., The Retail Equation

The economic climate is still uncertain for retailers. Although sales are improving, the National Retail Federation reports that fraudulent and abusive returns are on the rise, costing retail companies millions in profits. Additionally, shrink and organized retail crime continue to be multi-billion dollar retail problems.

As hazardous as this climate appears, it also presents an ideal opportunity for loss prevention professionals. By monitoring transactions over time and bringing statistics to bear, loss prevention analytics is reshaping operations and policies to protect bottom lines. This process often begins with “data mining”―a catch-all term for the methods analysts use to make sense of vast quantities of information. By sifting through millions of data points, analytics professionals are able to tease out relationships that would otherwise be undetectable. The result is that today’s retailers have a number of loss prevention tools that were unavailable only a few years ago. Below is an outline of some of the techniques used to maximize a retailer’s margin.

Challenging Basic Assumptions
Most retailers’ current accounting programs do not accurately reflect their real return rates; they often overlook exchange transactions and therefore understate the value and quantity of merchandise returning to the store. The return rates for 10 different retailers were recently tallied to analyze how they viewed the impact of merchandise returns. All were underestimating their return rate―one by as much as 150 percent. In fact, the 10 retailers studied saw an average return rate discrepancy of more than 80 percent.

Why is this important? Items and dollars that get returned within exchange transactions are unexpectedly hidden, masking retailers’ opportunities to rescue sales, prevent fraud, reduce shrink and more.

Predictive Modeling
As a retailer, imagine that each customer who returns a product hands you a slip of paper. On the paper is written a number between 0 and 100 percent and a note that says, “This number represents the probability that my return is fraudulent.” Although not this simple, this is the end result of predictive modeling. By tracking and analyzing customers’ purchases, exchanges, and return behaviors over time, loss prevention statisticians are able to develop real-time mathematical models that accurately estimate the chances of a return being legitimate or fraudulent. Recognizing high-risk customers is important, as it often leads to broader networks of return fraud.

Beyond Exception Reporting
Bringing computing power and statistics to the process of exception reporting is a key means of reducing fraud. Today, almost all retailers’ loss prevention departments use some form of exception reporting to identify suspicious transactions, individuals or employees. This process usually involves a complex set of rules to flag certain situations that “seem” problematic.

Taking this scenario one step further, the complex rules for flagging transactions can be reduced to a set of risk variables, each of which can be correlated to known outcomes. By determining the relationship between risk variables and known outcomes (such as correlating a certain employee’s behavior with his/her ultimate termination for fraud), retailers can learn which risk attributes are most important and what weight to assign each. Feeding these variables into predictive models then yields composite risk scores for evaluating transactions, employees, stores or other units of interest. This transition from complex rules to predictive models for identifying fraudulent transactions is analogous to a transition that occurred in the 1990s in the credit card industry: improved ROI and greater loss prevention efficacy let that market do more with less.

Fraud Ring Analysis
Social analysts find that people tend to group together based on similarities, and that this is particularly true among criminals. A key method of identifying (and ultimately cracking) organized retail crime rings is by first identifying high-risk customers, and then mapping out clusters of similar customers and analyzing their transaction behavior. Using sophisticated linking algorithms such as “fuzzy matching,” loss prevention analysts can connect known fraudsters to other questionable customers, often uncovering clusters of identities that constitute either crime networks or aliases of the same criminal.

Product Associations

Knowing how products are associated with one another allows them to be clustered into groups and ranked for risk. Combining this information with the typical shrink data goes far beyond the groupings one might find in a standard product hierarchy. For example, consider the capability to us a common product-pairing, like a digital camera and photo paper, to create an indirectly associated product-pair, like a digital camera and a photo album. Knowing this association and crossing the information with shrink data engenders risk profiling for many products and product clusters.

ROI Analysis
Before implementing any loss prevention strategy or solution, retailers should understand both the costs and associated benefits. Controlled tests, followed by statistical analyses, aid this understanding. Using “experimental” and “control” groups of stores―and tracking key metrics such as shrink, sales, return rates, or other important outcomes in before-during-after analyses―loss prevention professionals can accurately calculate a given strategy’s ROI. Controlled trials also let analysts manipulate elements that make up an overall strategy: By correlating changes in strategy with changes in ROI, statisticians can optimize loss prevention policies.

Clearly, statistics play a growing role in retailers’ approach to loss prevention issues and solutions. This is important in any economic climate; but in a mixed economy where profit margins are uncertain, it is imperative for retailers to have an unambiguous picture of their business that is rooted in solid statistical analysis.

David Speights, Ph.D., is the chief statistician and Christopher Hanks, Ph.D., is the senior statistician of The Retail Equation, the industry leader in retail transaction optimization solutions. The company’s applications use statistical modeling and analytics to predict consumer behavior and turn each individual shopper visit into a more profitable experience. Its software-as-a-service applications operate in more than 15,000 stores in North America, supporting a diverse retail base of specialty, department, sporting goods, auto parts and more. For more information, visit www.theretailequation.com.

Flash mobs: An increasingly common strategy for organized retail crime?

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Thursday, April 14, 2011

The majority of organized retail crime stories I've written about recently involve groups of thieves who travel around, hitting store after store after store. The I-95 corridor, for example, is a prime route for these gangs to travel because it's easy for them to hit multiple states and allude the jurisdiction of any one police department. The picture I've drawn in my head is of this group of hardened criminals, packed in a white van, plotting their next stop. But, turns out, that may not be the case at all.

I just read this Chicago Sun-Times article about a group of 70 youths who “stormed” a McDonald’s restaurant. It's actually unknown what this group was trying to do, other than cause the restaurant to voluntarily shutdown for three hours, but apparently this isn't the first time the Chicago police have dealt with flash mobs in the area:

“Both CPD and [Loyola] campus safety believe this activity is related to the same group of individuals who have attempted to create havoc in the area before,” wrote Robert Fine, the director of campus security for Loyola and a veteran Chicago cop, according to the article. “In February, we alerted you to a similar incident in which these ‘Flash Mob Offenders’ allegedly committed thefts within local retail stores around the Water Tower Campus community. The offenders exit the Chicago Red Line stop, they go to various shops or restaurants, usually clothing stores, and then storm the stores, taking as many items as they can carry. The incidents seem to occur most often on weekends, between 5 p.m. and 11 p.m.”

I'm wondering if this flash mob approach is becoming more common in retail theft than "traditional" organized retail crime. The theory, I'm guessing, is that if you show up with a huge group of people and grab as much as you can, the store can't possibly stop or even think about arresting everyone. Scary stuff if you're in loss prevention.

And, just in case you're really out of the loop, flash mobs have become a bit of a sensation in recent years, the most well known being gatherings of people in malls or other public spaces who sporadically perform some sort of act (usually a choreographed dance) and then disperse. It's quite entertaining, really. Usually these events are organized via social media like Twitter and Facebook. For your reference, here's my favorite from the Liverpool Train Station (and I think it's actually an ad, so it may not be a "real" flash mob, but it's entertaining):

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